Memory Specificity Analyses: Experiment 1 (Partial CF Learning Model)

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Import and process data

Memory

RL modeling

Choice weights from model with partial counterfactual learning

Figure: Distribution of choice weights from CF model

Model: Choice weights by block condition and age

	est
Predictors	Estimates	SE
abstraction1	-0.4781	0.0389
reward condition1	0.2408	0.0389
age scaled	0.1915	0.0511
abstraction1 × reward condition1	0.3327	0.0389
abstraction1 × age scaled	-0.0707	0.0390
reward condition1 × age scaled	0.0568	0.0390
(abstraction1 × reward condition1) × age scaled	0.0353	0.0390
Random Effects
σ2	0.92
τ00 subject_id	0.16
ICC	0.15
N subject_id	151
Observations	604
Marginal R2 / Conditional R2	0.291 / 0.399

Model: Exemplar choice weights across conditions

	est
Predictors	Estimates	SE
reward condition1	-0.0919	0.0441
Random Effects
σ2	0.59
τ00 subject_id	0.61
ICC	0.51
N subject_id	151
Observations	302
Marginal R2 / Conditional R2	0.007 / 0.513

Model: Category choice weights across conditions

	est
Predictors	Estimates	SE
reward condition1	0.5734	0.0529
Random Effects
σ2	0.85
τ00 subject_id	0.19
ICC	0.19
N subject_id	151
Observations	302
Marginal R2 / Conditional R2	0.241 / 0.382

Relations between choice weights and points earned

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_c_c)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -267.278  -36.123    4.793   38.580   96.301 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  104.105      7.636  13.634  < 2e-16 ***
## est           36.489      4.757   7.671 2.06e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57.31 on 149 degrees of freedom
## Multiple R-squared:  0.2831, Adjusted R-squared:  0.2783 
## F-statistic: 58.84 on 1 and 149 DF,  p-value: 2.056e-12

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_e_c)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -272.592  -35.015    8.594   44.639  132.400 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  136.796      8.487  16.117   <2e-16 ***
## est            8.763      4.177   2.098   0.0376 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 66.71 on 149 degrees of freedom
## Multiple R-squared:  0.02869,    Adjusted R-squared:  0.02218 
## F-statistic: 4.402 on 1 and 149 DF,  p-value: 0.03759

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_c_e)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -324.49  -45.41   -0.46   56.62  190.35 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  158.110      7.078  22.338   <2e-16 ***
## est           -7.549      6.693  -1.128    0.261    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 86.38 on 149 degrees of freedom
## Multiple R-squared:  0.008467,   Adjusted R-squared:  0.001812 
## F-statistic: 1.272 on 1 and 149 DF,  p-value: 0.2611

## 
## Call:
## lm(formula = total_points ~ est, data = beta_ests_points_e_e)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -326.12  -31.20   -5.67   44.45  222.81 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   53.035     13.394   3.960 0.000116 ***
## est           59.651      6.925   8.614 9.38e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 70.87 on 149 degrees of freedom
## Multiple R-squared:  0.3324, Adjusted R-squared:  0.328 
## F-statistic:  74.2 on 1 and 149 DF,  p-value: 9.382e-15

Figure 2D: Effect of choice weights on points earned

Relations between learning and memory

Do choice weights relate to memory?

Model: AUC by age, exemplar choice weights, specificity, block condition

	AUC
Predictors	Estimates	SE
age scaled	0.0100	0.0067
beta scaled	0.0189	0.0049
abstraction1	0.0611	0.0025
reward condition1	-0.0055	0.0026
age scaled × beta scaled	0.0138	0.0057
age scaled × abstraction1	0.0043	0.0026
beta scaled × abstraction1	-0.0064	0.0028
age scaled × reward condition1	-0.0009	0.0026
beta scaled × reward condition1	-0.0103	0.0033
abstraction1 × reward condition1	-0.0002	0.0025
age scaled × beta scaled × abstraction1	-0.0048	0.0031
age scaled × beta scaled × reward condition1	-0.0067	0.0036
age scaled × abstraction1 × reward condition1	0.0010	0.0026
beta scaled × abstraction1 × reward condition1	0.0004	0.0028
age scaled × beta scaled × abstraction1 × reward condition1	0.0042	0.0031
Random Effects
σ2	0.00
τ00 subject_id	0.01
ICC	0.61
N subject_id	151
Observations	608
Marginal R2 / Conditional R2	0.320 / 0.736

Figure 3C: AUC by exemplar choice weights - model effects

Model: AUC by age, category choice weights, specificity, block condition

	AUC
Predictors	Estimates	SE
age scaled	0.0190	0.0072
beta scaled	-0.0018	0.0042
abstraction1	0.0588	0.0029
reward condition1	-0.0067	0.0033
age scaled × beta scaled	-0.0028	0.0043
age scaled × abstraction1	0.0033	0.0029
beta scaled × abstraction1	0.0048	0.0029
age scaled × reward condition1	-0.0026	0.0034
beta scaled × reward condition1	0.0013	0.0036
abstraction1 × reward condition1	-0.0008	0.0029
age scaled × beta scaled × abstraction1	0.0009	0.0029
age scaled × beta scaled × reward condition1	-0.0058	0.0036
age scaled × abstraction1 × reward condition1	0.0005	0.0029
beta scaled × abstraction1 × reward condition1	0.0028	0.0029
age scaled × beta scaled × abstraction1 × reward condition1	-0.0038	0.0029
Random Effects
σ2	0.00
τ00 subject_id	0.01
ICC	0.63
N subject_id	151
Observations	608
Marginal R2 / Conditional R2	0.290 / 0.739

Figure 3C: AUC by category choice weights: model effects

Figure (for presentation): Category and exemplar memory by exemplar choice weights